Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease that results in a loss of cognitive functions. The early discovery of it can potentially stop or decrease the severity of AD. Extensive research has been conducted to find AD biomarkers. In recent years, due to the development of AI technologies and the ease of obtaining retinal images, various machine learning (ML)- and deep learning (DL)-based methods of identifying AD patients from these images have been proposed. These models are significant as they represent a potential screening tool for AD and a tool for identifying biomarkers from retinal images. This paper reviews the recent progress in this direction. It presents an overview of relevant methods and analyzes their strengths and limitations. Also, it discusses common challenges and possible future directions related to this topic. © 2025 by the authors.
| Original language | English |
|---|---|
| Article number | 4963 |
| Journal | Applied Sciences (Switzerland) |
| Volume | 15 |
| Issue number | 9 |
| Online published | 30 Apr 2025 |
| DOIs | |
| Publication status | Published - May 2025 |
Funding
This work was supported by the City University of Hong Kong (7020058).
Research Keywords
- Alzheimer’s disease
- deep learning
- machine learning
- retinal images
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/